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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

2,132 個評分
368 條評論


Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python....



Aug 25, 2016

excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.


Jan 17, 2017

Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.


226 - Machine Learning: Clustering & Retrieval 的 250 個評論(共 356 個)


May 20, 2018

Excellent - Goo

創建者 vivek k

May 25, 2017

awesome course!

創建者 Bruno G E

Sep 03, 2016

Simply Amazing!

創建者 Christopher D

Aug 09, 2016

Superb course!

創建者 Jinho L

Sep 20, 2016

Great! thanks

創建者 Pakomius Y N

Sep 29, 2020

Terima Kasih

創建者 Divyanshu S

Aug 27, 2020

Very helpful


Jul 30, 2020

very helpful

創建者 Manikant R

Jun 21, 2020

Great course


Apr 14, 2020

loved it..!!

創建者 Hanna L

Sep 02, 2019

Great class!

創建者 Mark h

Aug 08, 2017

Very helpful

創建者 邓松

Jan 04, 2017

very helpful

創建者 Jiancheng

Oct 27, 2016

Great intro!

創建者 Thuong D H

Sep 23, 2016

Good course!

創建者 Karundeep Y

Sep 18, 2016

Best Course.

創建者 Saurabh A

Sep 24, 2020

very good !

創建者 Pradeep N

Feb 22, 2017

"super one,

創建者 clark.bourne

Jan 09, 2017



Nov 11, 2018


創建者 Gautam R

Oct 08, 2016

Awesome :)

創建者 miguel s

Sep 20, 2020

very well

創建者 Neha K

Sep 19, 2020



Sep 17, 2020


創建者 Subhadip P

Aug 05, 2020